Method for calibrating antibiotic disk diffusion testing of microorganisms

ABSTRACT

A method for calibrating antimicrobic susceptibility testings of microorganisms comprises the steps of creating a histogram with a high response side and a low response side of isolates from a microbial species, which may contain resistant strains against an antimicrobial agent, calculating from the high response side of the histogram at least one statistical parameter, and defining a limit for susceptibility interpretation and comparative analysis of antimicrobic resistance, which is based on the statistical parameter. This limit separates susceptible strains from resistant strains against an antimicrobial agent.

This application claims priority to PCT/SE02/00688 filed 8 Apr. 2002 andto Swedish Application Ser. No. 010125 1-7 filed on April 2001.

The present invention relates to the interpretion of antimicrobicresistance in routine susceptibility tests. More precisely, theinvention relates to a method for calibrating antimicrobicsusceptibility testings of microorganisms.

BACKGROUND

Antibiotic resistance surveillance becomes more and more important in aglobal situation of increased occurrence and spread of resistance genesamong bacterial pathogens. National as well as internationalsurveillance of antibiotic resistance is thus urgently needed in thepresent increase of resistance worldwide.

Antimicrobial susceptibility testing (AST) constitutes one of the mostimportant methods of analysis in a clinical microbiology laboratory.Correct information regarding the most appropriate antimicrobialagent(s) to be used as empirical therapy in for example an acuteclinical situation of an infected patient requires thorough knowledge ofthe antimicrobial susceptibility levels in relation to the bacterialspecies and the type of infection in question.

In addition, when a causative bacterial strain has been isolated fromthe site of infection the antimicrobial test results provide a rationalbasis for either continued therapy or a change to a more effective drug.

Different types of susceptibility tests can be used to test theantimicrobic susceptibility of a microorganism. One type ofsusceptibility test is the disk diffusion test. This is a standardizedtest, in which a plate containing a growth medium in agar gel isinoculated with a microbial isolate and one or more disks impregnatedwith fixed concentrations of antibiotics are placed thereon. Afterappropriate incubation, the diameter of zones of inhibition around thedisks (if present) are registered in order to determine the sensitivityof the inoculated microorganism to the particular antimicrobial agentimpregnated in each disk.

Another type of susceptibility test is the broth microdilution test. Inthis type of test, dilutions of antibiotics are prepared in tubes ormicrowells. Each tube or well with various concentrations ofantibiotics, usually as a twofold dilution series, is inoculated with astandardized suspension of a particular microorganism. After incubation,the wells or tubes are examined for turbidity, haze and/or pellet andcompared with a growth control as well as a non-inoculated control. Theminimum concentration of antimicrobial agent that prevents visiblemicrobial growth is calculated as the Minimal Inhibitory Concentration(MIC).

In spite of the availability for several years of automatedmicrodilution methods, the disk diffusion method is still the mostwidely used susceptibility testing procedure in most countries. The testis performed according to some standardized methodology issued by areference group, such as NCCLS (National Committee for ClinicalLaboratory Standards), SRGA (Swedish Reference Group for Antibiotics),the French “Comité de l'Antibiogramme de la Société Francaise deMicrobiologie”, the British “Working Party on Antibiotic SensitivityTesting of the British Society for Antimicrobial Chemotherapy”, theAustralian “ASIG Antimicrobials Special Interest Group”, the German DINgroup, etc.

The results of this test are semi-quantitative. In. 1979 the SRGAintroduced the 3 susceptibility categories which are still in use today:S (susceptible), I (intermediate) and R (resistant) in dependence of thesize of the inhibition zone.

However, the definitions and interpretive criteria for resistance varywith the guidelines adhered to as do those test results which areinfluenced by differences in test performance. The differences areparticularly paramount for disk test results, which means that themajority of antibiotic susceptibility test results cannot be utilizedfor surveillance purposes.

Most surveillance efforts have relied on MIC tests because of the higheraccuracy of such methods. The adherence to disk diffusion standards areoften incomplete and the results unreliable. In one investigation usingthe disk method, all participating laboratories sent their bacterialisolates to one single laboratory where the disk tests were performed inorder to ensure comparable results from the different geographicalregions. Such an approach will be impossible if resistance surveillanceis to be performed on a larger scale globally.

In general, organisms are considered susceptible to an antibiotic whenthey are inhibited by concentrations easily achieved in vivo and whenthe clinical efficacy has been documented. This implicates a correlationbetween the MIC and the clinical outcome. Similarly, organisms aretermed resistant when concentrations required for inhibition are higherthan those easily obtained in vivo, indicating that treatment with thisantibiotic is not likely to be successful.

The statistical parameters for the inhibition zones obtained forindividual bacterial species have been investigated (Kronvall et al.,APMIS 99:887-892, 1991). At this time the bacterial species studied didnot exhibit a considerable resistance to the antibiotic of choice,gentamicin. In order to be able to study a homogenous population anyresistant outliers were eliminated, and the statistical parameters wereonly determined for those strains which belonged to the susceptiblepopulation.

When antibiotic susceptibility test results, such as inhibition zonediameter values, are analyzed separately for each antibiotic andbacterial species, including resistant strains, some special featuresbecome apparent. The position of a normal wild-type population of zonediameters in a given laboratory will stay remarkably stable over theyears, reflecting the stability of a routine method in daily use.However, when results are compared between different laboratories, theposition of a normal population might vary considerably. Theseinter-laboratory differences are the main reason for the relative lackof accuracy of the disk diffusion test.

One approach to solve the problem of interlaboratory variation has beento introduce a reference or control strain, which serves as a calibratorstrain. Control strains are recommended by reference authorities forquality control and therefore such strains are usually already availablein the laboratory.

A calibration is a regular procedure in any clinical chemistry test andinvolves the use of a calibrator with a known concentration of thesubstance to be measured. The calibration of the test in the individuallaboratory using their own equipment and reagents is then controlledusing control samples, either internal controls or from an externalquality control agency.

One method for calibration of the disk diffusion test is called“reference strain corrected breakpoints” (or “control strain peakcorrection”) and requires the regular testing of an internationalreference strain in the laboratory (Kronvall et al., Antimicrob. AgentsChemother. 32:1484, 1988). The position of the zone diameter peak in thereference laboratory has to be known and the difference in mm value fromthis peak value to the interpretive break-points can be calculated. Asimilar relation should exist in the individual laboratory between itscontrol peak value and the breakpoints, which should be adjustedaccordingly. This method improved the accuracy of the disk test byreducing false-resistant interpretations from 4.4% to 2.3%.

A further improvement was obtained by using a second method, singlestrain regression analysis (Kronvall, J. Clin. Microbiol. 16:784, 1982).This method, called SRA, provides a calibration of the test, which istruly species-related and laboratory-specific. However, it both requiresespecially prepared disks with different disk potencies and computerprograms for the calculations. This method further reducedfalse-resistant results down to 0.14% in the studies referred to above.

SUMMARY

The purpose of the invention is to achieve a method for calibratingantimicrobic susceptibility testings of microorganisms whereby theabove-mentioned problems are eliminated.

Normalized or standardized interpretation of antibiotic resistance inbacteria from disk diffusion test results should relate to some stablecharacteristic of these test results. The position in the zone diameterhistogram of the normal population of susceptible or wild type strainsis such a stable characteristic that is available in every laboratory.The position of a single reference or control strain—which is testedrepeatedly in the same manner as clinical isolates of the same orsimilar species—also represents such a stable characteristic which canform a basis for an internal calibration for normalization orstandardization of interpretation of antimicrobicsusceptibility/resistance.

The inventive method comprises an interpretation of a conventionalhistogram of in vitro susceptibility testings with isolates from amicrobial species, which may contain resistant strains against anantimicrobial agent. The susceptibility test data are plotted on they-axis and primary test measurement data for the antimicrobial agent areplotted on the x-axis. The susceptibility test data can be plotted asnumber of isolates or as percent isolates of microbial species.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a histogram of Escherichia coli isolates againstnorflaxacin.

FIG. 2 is histogram similar to FIG. 1, except taken eleven years later.

FIG. 3 shows the histogram of FIG. 1 with interposed calculated curve.

FIG. 4 shows the histogram of FIG. 2 with interposed calculated curve.

FIG. 5 shows isolates of E. coli tested against gentamicin in twodifferent laboratories with interposed calculated normalizeddistributions.

FIG. 6 shows a graph of normalized mean values versus true mean valuesof control strains.

DETAILED DESCRIPTION

The microbial species to be tested can be any cultivable microorganism,such as a bacterial species or a fungal species. In order to obtainspecies-related and laboratory-related interpretive breakpoints, ahistogram of for example clinical isolates can be used. The method isespecially suitable for analyzing inhibition zone diameters from diskdiffusion tests with clinical isolates from a bacterial species, whichmay contain resistant strains against an antibiotic. Thus, no outliersare excluded. The number of isolates or percent isolates are plotted onthe y-axis and the growth inhibition zone diameter values from paperdisks impregnated with the antibiotic are plotted on the x-axis. Whenthe method of O'Brien et al. (J. Am. Med. Assoc. 210:84, 1969) is used,histograms are obtained with percent isolates on the y-axis and zonediameter values from 6 mm (paper disk diameter) to 60 mm on the x-axis.However, the inventive method is equally well adapted for amicrodilution tests, the concentration data being minimal inhibitoryconcentrations. All data can be computerized.

The basis for the present invention relates to a reconstruction of theoriginal population which is susceptible to the antibiotic tested. Inthis connection certain relevant features of a corresponding histogramof isolates are utilized. When for example zone diameter histograms areproduced for individual bacterial species and antibiotics, thedistribution of zone diameter values will be rather restricted, and ahomogeneous population will be formed for the naturally susceptibleisolates without resistance. The high zone side of the normal populationof susceptible strains therefore remains unchanged by the occurrence ofresistant isolates. This provides the internal reference which makeshistograms for the same combination of antimicrobial and bacterialspecies from any laboratory comparable. When clinical isolates of anycombination of antibiotic and individual bacterial species are analyzedsimilarly, the position of the normally susceptible wild-type strains isunchanged whereas the resistant or intermediately resistant strains formmore or less well defined populations at the lower end of the zonediameter spectrum.

When a homogeneous wild-type population of isolates or strains of agiven species is analyzed by means of parametric and non-parametricmethods populations are obtained which are slightly peaked and skewedtowards higher inhibition zone diameter values as compared to theGaussian distribution. The homogeneity is relative, but for theinvention, they can be regarded as homogeneous. The resulting zones ofinhibition exhibit a restricted range of values, which approximate anormal distribution (standard Gaussian distribution). However, theinvention also applies to other kinds of probability distributions.Parametric tests, such as mean values and standard deviations, cantherefore be used with negligible errors in order to describe thedistribution. Thus, a probability distribution can be calculated fromthe histogram, and from this probability distribution at least onestatistical parameter is calculated which can be used as an internalcalibration of the disk diffusion test on the bacterial speciesexamined. Then a susceptibility interpretation and a comparativeanalysis of antibiotic resistance can be accomplished. Parametriccriteria can then be introduced in order to define interpretativebreakpoints.

Such a normal population or a similar population of a control strainthus has the basic characteristics for any given combination ofmicrobial species and antimicrobial agent. A limit for susceptibilityinterpretation and comparative analysis of antimicrobic resistance, e.g.normalized resistance breakpoint, can then be determined in severaldifferent ways.

According to the invention, at least one statistical parameter iscalculated from the high response side of a histogram, preferably theupper part thereof. Such a statistical parameter of the histogram can bea peak value in the susceptible range, which can serve as an indicatorof the normal population. Thus, the position of a normal population withno resistant isolates in a zone diameter histogram can be calculated asthe mean value of the zone diameters. Alternatively, this position canbe determined as the zone diameter with the maximum number of isolates.The median value can also be used for this purpose.

A limit for susceptibility interpretation and comparative analysis ofantimicrobic resistance can then be defined, which is based on thestatistical parameter taken from that part of the histogram only whichcomprises the high zone diameters. Susceptibility zone breakpoints forS, R, or I, respectively, can for example be obtained by subtractingdefined millimeter values from the median value or the arithmetic mean.

Thus, when the peak position has been established in an individuallaboratory for the normal population of strains of a given speciestested against an antibiotic, then a normalized breakpoint forresistance can be determined in several ways. The peak position cansimply be subtracted with a specific figure. This figure should bedifferent for different antibiotics and should represent a normal limitfor the susceptible normal population. This figure for subtractionadjustment can also be a function of the normal peak position.

However, it is preferred that the position of the peak between a highzone diameter distribution side and a low zone diameter distributionside (or corresponding high and low MIC values) is determined by meansof more mathemtically precise methods. This also applies to thedetermination of a standard deviation.

In this case, the inventive method utilizes the observation that whenantimicrobic resistance to an antimicrobial agent occurs among strainsof a certain species the configuration of the histogram will change.Those isolates, which exhibit resistance, will inevitably producesmaller zones of inhibition, and they will often aggregate inpopulations separate from the original, normal population. Sometimes,they will also form a shoulder on the lower side of the normalpopulation.

In other words, the acquisition of antimicrobic resistance genes ormutations reflect evolution in that isolates are always produced havinginhibition zones (or MIC values), which are smaller (or larger) thanthose of the normal wild-type population. This means that incorresponding histograms the part representing the high zone values tothe peak will always be unaffected by the development of resistance.Consequently, if this part can be properly characterized, then ahypothetical normal population of strains can be reconstructed in spiteof the development of resistant isolates. An accurate characterizationof this hypothetical normal population in a histogram of isolates from amicrobial species—which may contain resistant strains against anantimicrobial agent—then makes it possible to estimate the number ofstrains which deviate from this population. Thus, a normalized way ofdefining antibiotic resistance is provided, which can be utilized forcomparative surveillance purposes.

Such a normalization (or in this connection standardization) of aprobability distribution has primarily a comparative purpose. It shouldnot be confused with the interpretation into susceptibility categories,which a laboratory has to perform in order to provide a report to therequesting physician. In this case, an interpretation is performedaccording to the standardization of the methodology issued by areference authority. The present method of normalization is intended toprovide a uniform identification of resistance irrespective of themethodological standard used in the laboratory. Thus, by using themethod for calibrating antimicrobic susceptibility testings ofmicroorganisms according to the invention, a basis is provided foranalyzing disk diffusion test results world-wide, levels of antibioticresistance being compared.

The two parameters, which have to be characterized, are the peak of thenormal population and the upper curve of increasing numbers of strainsup to this peak. A normalized Gaussian distribution of a theoreticalnormal population can then be construed from the position of the zonediameter peak, the high zone diameter distribution side, and its mirrorimage. Such a normalized Gaussian distribution can be construedgraphically by means of graphical models, such as undirected graphicalmodels or directed graphical models. Such models are known by theskilled man within the arts of probability theory and graph theory.

However, a preferred way of defining a position of the zone diameterpeak between a high zone diameter distribution side and a low zonediameter distribution side is described below for a normalized Gaussiandistribution of a theoretical normal population. In this procedure, atotal number for the theoretical normal population is construed.

Firstly, a slope is calculated for a line through the percent isolatesof adjacent zone diameter values. Preferably, this is performed bystarting on the high zone diameter distribution side of the histogram.

Secondly, a shift in slope direction is detected. The zone diametervalue for this shift then represents the position of the zone diameterpeak, the mean value of the zone diameters being defined.

Thirdly, the sum of half the number of isolates at the position nowdefined plus the number of isolates having higher zone diameter valuesis then calculated, and this sum is then doubled. In this way, the totalnumber of isolates can be calculated for the theoretical normalpopulation, i.e. for the high zone diameter distribution side from themiddle of the position of the zone diameter peak and the mirror imagethereof. Of course, a total percentage of isolates for a histogram canbe calculated in a similar way.

In a preferred embodiment of the invention a computer program firstchecks the histogram from the uppermost zone values, weighted movingaverages being calculated. Such averages can be determined by usingstrain numbers for two or more zone diameters. In order to make the testsensitive enough two zone averages are preferred. However, a betteraverage is obtained if more zone data are included, but the detection ofa shift from increasing averages towards a decreasing value then becomesless sensitive. It is also preferred that an increasing average isimmediately followed by two decreasing average values in sequence, theposition of the zone diameter peak being accurately established.

Of course, the calculation of averages can also be changed. For example,in laboratories with less precision an average should be calculated onthree or four values instead of two.

When the computer program has registered a shift as described, theprevious zone diameter is taken as the peak, i.e. the zone diameter ofboth the previous higher average and the last and lower average. With aweighted average including data for more than two zone diameter values,the zone diameter taken as the peak is shifted further up. The programthen calculates the number of isolates with zone diameters higher thanthis peak zone plus half of the number of isolates at the peak zonediameter value. This total number represents the theoretical half of thenormal population.

After resolving the total number for theoretical normal population, thenormalized Gaussian distribution of the theoretical normal populationhas to be determined. A preferred procedure is described below. Firstly,for each zone diameter value on the upper half of the normal populationa percentage value is calculated as percent of the theoretical totalnumber.

Secondly, an accumulated percentage value is calculated for each numberof isolates (or percent isolates) on this high zone diameterdistribution side of the upper half of the normal population.

These percentage values can be estimated by commencing from isolateshaving the highest zone diameter values and then add up the number ofaccumulated isolates. The corresponding percentage of the theoreticaltotal number is then calculated. Preferably, the order of theseaccumulated percentages values are then reversed, i.e. in the direction100% to 0%.

Thirdly, a linearized transformation of these accumulated percentagevalues against the zone diameter values is performed. Such atransformation is performed in order to transform a curvilinearrelationship to a linear relationship.

Preferably, a probit transformation is utilized, in which the reversedaccumulated percentages are converted to probit-values. This can beaccomplished by using a computer having a program for such conversionpurposes.

At last, the equation constants are calculated for the linearrelationship between the probit values and zone diameters. Preferably,this calculation is performed by using the least squares method.

When the linear relationship has been determined for the probittransformed percentage values against the inhibition zone values, thestandard deviation (SD) can be calculated from the probittransformation. In this connection, the center of the linearrelationship is probit 5.0, which corresponds to the mean. Thedifference between the zone value for probit 6.0 and this mean thenrepresents 1 SD.

Alternatively, a theoretical curve can be calculated from the normalizedGaussian distribution. For this calculation the percentages for thein-between zone values (i.e. 30.5, 31.5, 32.5, etc.) can be used, andthe values of the difference in percent are then used in order to plot atheoretical Gaussian distribution of the histogram for the normalpopulation of strains. Then the mean value and the standard deviationcan be calculated.

EXAMPLES

The invention will now be further described and illustrated by referenceto the following examples. It should be noted, however, that theseexamples should not be construed as limiting the invention in any way.

Example 1 Evaluation of Antibiotic Resistance with Time

FIG. 1 shows a histogram of 1988 from Karolinska hospital in Sweden, inwhich histogram Escherichia coli isolates were tested againstnorfloxacin (disk content 10 μg). It can be seen that the zone diametervalues for these clinical isolates cluster around 29-30 mm with adistribution of ±8 mm.

The results from 1988 in FIG. 1 were compared with similar resultseleven years later (FIG. 2). In this histogram a number of isolates nowappear, which have inhibition zones all the way down to no zone, thesize of the 6 mm disk. The comparison was accomplished by means ofcomputer analysis according to the inventive method for calibratingantimicrobic susceptibility testings of microorganisms.

First, the slope downwards was tracked and the shift to decreasingvalues at the peak position identified by comparing sliding means (eachbased on two zone diameter figures). Then, the percentages of theisolates of the upper half of the population were determined andconverted to probit values. A regression using the least squares methodwas then performed and the theoretical percentages for the wholepopulation calculated. The histograms with the calculated theoreticalcurves interposed are shown in FIGS. 3 and 4, respectively.

The mean value of the initially measured histogram data from 1988 (FIG.3) was first calculated according to a customary statistical procedureand was found to be 29.16 mm (SD 3.22). When the same histogram then wasanalyzed as a normalized Gaussian distribution of a theoretical normalpopulation by means of the method according to the invention, thecorresponding mean was found to be 29.27 mm with a standard deviation(SD) of 3.25. The total number of isolates was 3756.

Eleven years later the mean value of all 5944 isolates (FIG. 4) wasfound to be 28.24 mm (SD 7.39) according to the customary statisticalprocedure. The normalized Gaussian distribution of a theoretical normalpopulation, however, exhibited a mean value of 30.49 mm with a standarddeviation (SD) of 3.22. Thus, the occurrence of resistant strains withlow zone values is reflected by the increase in standard deviation forthe mean of all strains as well as by a slightly lower mean value.

When the calculated theoretical reference population of normal isolateswas used to set a limit for resistance, the 1988 limit was 19 mm and the1999 year limit for resistance was 20 mm. Thus, the slight shift in theposition of the peak during these 11 years is automatically observed andcorrections are included in the zone breakpoints for comparativeanalysis of resistance. The percent resistance for these two populationswould then be 0.56% for the 1988 population and 10.8 for the 1999population, an increase which is in agreement with the true findings.

In this calculation, it is assumed that 3 SD below the mean includes99.86 percent of the normally susceptible strains in this group. Asuitable limit for susceptibility interpretation and comparativeanalysis of antimicrobic resistance could then be defined as three timesthe standard deviation below the mean value.

Although the two histograms from 1988 and 1999 exhibit considerabledifferences, the present method of histogram normalization produced aninternal calibration (standardization) of the test, which will permit acomparative analysis of resistance. Thus, the theoretical curve with itsmean value and standard deviation can be used for setting a breakpoint,which will not be affected by interlaboratory differences. Nor will thebreakpoint be affected by the different frequencies of resistantisolates, which appear in different histograms.

In spite of several years of data collection from antimicrobicsusceptibility testings of microorganisms in many centers worldwide, amethod has not yet been provided whereby results form differentlaboratories can be compared. The inventive method makes it possible toutilize the worldwide abundance of for example results from diskdiffusion tests, which cannot be utilized for surveillance because oflack of comparability of the data. Furthermore, the invention satisfiesthe urgent need for another method to “calibrate” the test or to“normalize” the susceptibility test results for comparative purposes.The increasing number of exceptions to the regular zone breakpoints inthe NCCLS list verifies this need.

In addition, additional control or reference strains are not necessaryfor calibration and normalization of the interpretation. All disk testresults all over the world can be made comparable and be included insurveillance studies by means of the method according to the invention.

Example 2 A Comparison Between Different Laboratories

The potential of the present invention, whereby results fromlaboratories in different-parts of the world are comparable.

In this example histograms of zone diameter values from disk testresults for E. coli tested against the antibiotic gentamicin wereanalyzed according to the principle of normalized resistanceinterpretation. One laboratory (KS) used the SRGA (Swedish ReferenceGroup for Antibiotics) methodology and a disk content of 10 μggentamicin, whereas the other laboratory on a different continent (AR, aclinical microbiology laboratory in South America) used the NCCLS(National Committee for Clinical Laboratory Standards) standardizationand a 5 μg gentamicin disk.

The different methods used result in different positionings of themajor, susceptible populations of strains (FIG. 5). As seen, the methodacccording to the present invention results in two completely differentnormalized distributions. Consequently, different R-limits forresistance are obtained, i.e. KS: R<21 mm and AR: R<17 mm, respectively.These zone breakpoints for normalized interpretation reflect the truesituation with reference to gentamicin resistance in the twolaboratories.

Example 3 A Comparison Between True and Calculated Means for theValidation of the Inventive Normalization procedure

The inventive method of normalized calculation of a susceptiblepopulation of a given bacterial species was validated by using resultsobtained from repeated tests of reference strains. In this connection itis a normal procedure every day in a clinical microbiology laboratory totest so called control or reference strains, for example Staphylococcusaureus, ATCC 29213, and Escherichia coli, ATCC 25922 (ATCC=American TypeCulture Collection), in disk diffusion tests with reference to theirantibiotic susceptibility. The inhibition zone diameter values obtainedfrom each such an individual reference strain form a statistical normaldistribution. Thus, the mean value and standard deviation can becalculated directly from the experimental results obtained.

The true mean values from the control strains were compared with theresults obtained from corresponding laboratory strains by means ofnormalized calculations according to the present invention, only thehigh zone values in the histograms being used.

As shown in FIG. 6, a very close correlation between the true means ofthe reference bacterial species and the calculated normalized means fromcorresponding laboratory strains was obtained with a correlationcoefficient r=0.998.

1. A method for calibrating antimicrobic susceptibility testing data ofmicroorganisms, wherein the method comprises the steps of creating ahistogram based on in vitro susceptibility test data against anantimicrobial agent for isolates from a microbial species, whichmicrobial species isolates may contain unknown resistant strains againstsaid antimicrobial agent wherein the number of isolates of microbialspecies or percentage of the total number of isolates of microbialspecies is on a y-axis of said histogram and registered response valuesagainst said antimicrobial agent on a x-axis of said histogram;determining a position of a response peak of the histogram between ahigh response side and a low response side corresponding to isolates ofmicrobial species susceptible to said antimicrobial agent; calculatingfrom said high response side of said histogram at least one statisticalparameter; and defining a limit for susceptibility interpretation andcomparative analysis of antimicrobic resistance, which is based on saidat least one statistical parameter; said limit separating susceptiblestrains from resistant strains against said antimicrobial agent. 2.Method as in claim 1, wherein said isolates arc clinical isolates. 3.Method as in claim 1, wherein said at least one statistical parameter isstatistically obtained from a probability distribution calculated fromsaid high response side of said histogram.
 4. Method as in claim 1,wherein said microbial species is a bacterial species or a fungalspecies.
 5. Method as in claim 1, wherein said antimicrobial agent is anantibiotic.
 6. Method as in claim 1, wherein said antimicrobicsusceptibility testing is a Minimal Inhibitory Concentration test, saidregistered response values being minimal inhibitory concentrations. 7.Method as in claim 1, wherein said antimicrobic susceptibility testingis a disk diffusion test, said registered response values beinginhibition zone diameter values from paper disks impregnated with saidantimicrobial agent.
 8. Method as in claim 3, wherein said probabilitydistribution is a Gaussian distribution.
 9. Method as in claim 8,wherein said at least one statistical parameter is the mean, defining aposition of the statistical response peak between a high statisticalresponse distribution side and a low statistical response distributionside, and/or standard deviation.
 10. Method as in claim
 9. wherein saidlimit is defined as three times said standard deviation below said mean.11. Method as in claim 9, wherein said Gaussian distribution is astandard Gaussian distribution.
 12. Method as in claim 9, wherein saidGaussian distribution is a normalized Gaussian distribution of atheoretical normal population.
 13. Method as in claim 12, wherein saidnormalized Gaussian distribution is construed from said position of saidstatistical response peak, said high statistical response distributionside, and its mirror image.
 14. Method as in claim 12, wherein a totalnumber for said theoretical normal population is construed by aprocedure comprising the steps of calculating a slope for a line throughsaid number of isolates of adjacent registered response values startingon said high response distribution side; detecting a shift in slopedirection, which represents the position of said statistical responsepeak, thereby defining said mean; and calculating said total number asthe doubled sum of half the number of isolates at said position plus thenumber of isolates having higher response values.
 15. Method as in claim13, wherein said normalized Gaussian distribution is construedgraphically.
 16. Method as in claim 14, wherein said normalized Gaussiandistribution is determined by a procedure comprising the steps ofcalculating for each registered response value a percentage value aspercent of said total number; calculating accumulated percentage valuesfor each of said number of isolates or percent isolates on saidstatistical high response distribution side; performing a linearizedtransformation of said accumulated percentage values against saidstatistical response values; and calculating the equation constants fora linear relationship obtained from said transformation; therebydefining said normalized Gaussian distribution of said theoreticalnormal population.
 17. Method as in claim 15, wherein said graphicalconstruction is performed by means of an undirected graphical model or adirected graphical model.
 18. Method as in claim 16, wherein saidcalculation of equation constants is performed by means of the leastsquares method.
 19. Method as in claim 16, wherein said calculation ofaccumulated percentage values is performed in the direction 100% to 0%.20. Method as in claim 16, wherein said transformation k a probittransformation.
 21. Method as in claim 20, wherein said standarddeviation is calculated from said probit transformation.